This thesis contributes to the development of measures of model selection and model adequacy for mixed-effects models. In the context of linear mixed-effects models, we review and compare in a simulation study a large set of measures proposed to evaluate model adequacy or/and to perform model selection. In the more general context of generalized linear mixed-effects models, we develop a measure of both model adequacy and model selection, that we name PRDpen. As a measure of model adequacy, our proposition gives information about the model at hand, as it measures the proportional reduction in deviance due to the model of interest in comparison with a prespecified null model. Furthermore, as a measure of model selection, PRDpen is able to choose the model that best fits the data among a set of alternatives, similarly to the information criteria.